Last Updated On: 2022-09-19 19:23:12.063245 Author: Philipe Bujold
Two areas of focus: End consumers and key influencers (not just in social media terms)
One quick survey to rule them all. The topics covered will be:
Exploratory:
Comparisons:
c:\Anaconda\envs\pandas-profiling\lib\site-packages\IPython\core\interactiveshell.py:3457: DtypeWarning: Columns (5,168,233) have mixed types.Specify dtype option on import or set low_memory=False.
Once imported, the data it filtered to keep only those records that were considered complete, and for participants aged over 18.
Original dataframe lenght: 4180 ... Lenght after filtering: 4180
Columns that contain string responses (i.e., non-categorical data that must be transformed)
Index(['Vrid', 'Vstatus', 'Vcid', 'Vcomment', 'Vlanguage', 'Vreferer',
'Vsessionid', 'Vuseragent', 'Vip', 'Vlong', 'Vlat', 'VGeoCountry',
'VGeoCity', 'VGeoRegion', 'Vpostal', 'RID', 'var428', 'var200O1406Othr',
'var201O1415Othr', 'var212O1440Othr', 'var218', 'var237', 'var238',
'var261', 'var427O2785Othr', 'var269', 'var289O1890Othr', 'var290',
'var362O2240Othr', 'var364O2246Othr', 'var365O2257Othr',
'var452O2784Othr', 'var376O2327Othr', 'var441O2711Othr', 'zip',
'gender_self_describe', 'hispanic_other', 'var306O1943Othr',
'politics'],
dtype='object')
Alchemer did a few mishaps when ordering data, manually reordering some of the yes/no answers.
reordered question:'Are you actively trying to reduce your food waste?' reordered question:'Does your household pay for their own electricity?' reordered question:'Before taking this survey, had you considered using community solar in the last 12 months?' reordered question:'If people have the choice, should they choose community solar because it is the right thing to do?' reordered question:'Does your household pay for their own electricity?' reordered question:'Before taking this survey, had you considered using green energy in the last 12 months?' reordered question:'If people have the choice, should they choose a green energy provider because it is the right thing to do?' reordered question:'You noted that you are trying to eat less or no meat Is this something you’ve shared with others?' reordered question:'Are there any people or groups of people in your life you think feel strongly against eating less (or no) meat?' reordered question:'Before taking this survey, have you ever considered renting a fully electric car? Car rentals can be daily or hourly rentals' reordered question:'Do you think that people who rent cars should rent fully electric cars because it is the right thing to do?' reordered question:'Before taking this survey, have you ever considered getting a fully electric car?' reordered question:'Do you think that people should drive a fully electric car because it is the right thing to do?'
The sample is slightly skewed from general US census data (as well as SASSY data) - reweighing will correct this.
All data averages are reweighed on ethnicity and SASSY scores.
To understand if the general population (not social media influencers) believes there is a reputational risk associated with posting on social media about climate change, and how that compares to posting about activities that are environmentally positive but not explicitly climate-motivated.
What we want to be able to say
| Snapchat | TikTok | YouTube | Other - Please specify | I don't use any social media | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
| What social media platforms do you use? Select all that apply | 80.46 | 51.13 | 32.46 | 16.96 | 28.08 | 35.60 | 31.47 | 72.10 | 3.93 | 4.02 |
| What social media platforms do you use daily? Select all that apply | 70.12 | 35.64 | 12.88 | 8.12 | 18.55 | 26.59 | 18.68 | 51.87 | 3.64 | 5.65 |
| What social media platforms do you post or share content to? Select all that apply | 71.86 | 35.54 | 9.03 | 6.09 | 16.75 | 15.48 | 17.54 | 20.17 | 3.98 | 8.28 |
We can see here some of the other social media that respondents used, as well as the demographic information of said respondents.
| education | income | age_group | SASSY | politics | state | sex | Other SMs used | Other SM used daily | Other SM posts on | |
|---|---|---|---|---|---|---|---|---|---|---|
| Loading... (need help?) | None | education | income | age_group | SASSY | politics | state | sex | Other SMs used | Other SM used daily | Other SM posts on |
| Health and Fitness | Personal finance (money saving strategies, life hacks, etc ) | Extreme weather (floods, heatwaves, storms, etc ) | Technology and gadgets | Climate change | Food (new restaurants, recipes, ingredients, etc ) | Lifestyles (Travel, hobbies, work ) | |
|---|---|---|---|---|---|---|---|
| Thinking about the social media you use, how interested are you when you see social media content about | 0.48 | 0.46 | 0.53 | 0.48 | 0.42 | 0.56 | 0.49 |
| Thinking about your typical posting habits, how likely are you to use social media in the future to post or share content with others about | 0.48 | 0.41 | 0.49 | 0.47 | 0.41 | 0.58 | 0.55 |
c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
| Health and Fitness | Personal finance (money saving strategies, life hacks, etc ) | Extreme weather (floods, heatwaves, storms, etc ) | Technology and gadgets | Climate change | Food (new restaurants, recipes, ingredients, etc ) | Lifestyles (Travel, hobbies, work ) | |
|---|---|---|---|---|---|---|---|
| How likely is it that you will receive a positive reaction (content sharing, likes, positive comments, direct messages, etc ) if you posted on social media about | 0.56 | 0.50 | 0.54 | 0.55 | 0.47 | 0.65 | 0.60 |
| How likely is it that you will receive a negative reaction (content sharing, dislikes, negative comments, direct messages, etc ) if you posted on social media about | 0.38 | 0.39 | 0.40 | 0.38 | 0.44 | 0.36 | 0.37 |
To understand what food waste avoidance strategies people are already employing.
What we want to be able to say
Below you can see how many respondents are actively trying to reduce food wastem as well as a table showing the steps that those who are trying to reduce waste are taking.
| education | income | age_group | SASSY | politics | state | sex | Can you tell us a bit more about the steps you are taking to reduce food waste? | |
|---|---|---|---|---|---|---|---|---|
| Loading... (need help?) | None | education | income | age_group | SASSY | politics | state | sex | Can you tell us a bit more about the steps you are taking to reduce food waste? |
| We all do different things when it comes to shopping for food and cooking How often do you do the following behaviors? | |
|---|---|
| I make a grocery list when I go grocery shopping | 0.699223 |
| I stick to use-by/best-before labels | 0.664936 |
| I adjust plans for what I am cooking based on what I have in the fridge | 0.672528 |
| I compost food scraps / other food waste instead of putting it in the garbage | 0.296812 |
| I check my fridge and pantry before buying food | 0.752424 |
| I pack my fridge to the brim | 0.394508 |
| I make a point to use up food that is about to expire | 0.717997 |
Resondents were given a 7-item likert scale for which to answer from Strongly agree to Strongly disagree, the table below showes the weighted average of normalized responses. That is, 1 represents an average responses of Strongly agree, whilst 0 represents an average response of Strongly disagree.
| How much do you agree or disagree with the following statement? | |
|---|---|
| It is more environmentally sustainable to waste less food | 0.777686 |
| It is important to avoid wasting food to help alleviate climate change | 0.652881 |
| It saves me money to waste less food | 0.854913 |
To understand if the national index values for buying green energy also apply to community solar
What we want to be able to say
c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
Below you can see a comparison of normalized, weighted averages for each question that was asked on the topic of energy. Since we wanted to know whether answers in the Community Solar condition were equivalemnt to those in the Green Electricity condition, we set a bound of +/- 10% on the green electricity average and contrasted the community solar average to it. A significant difference therefore represents a difference in %responses greater than +/- 10%.
| Outcome efficacy from purchasing | Outcome efficacy from installing | Responsibility belongs supply-side | Consideration | Adoption | Belief that others have adopted | Belief that others should adopt | Belief that others think people should adopt | Cost relative usual | Intention | Perceived community benefit | Perceived personal benefit | Self-efficacy | Program interest | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Community Solar | 0.587223 | 0.651333 | 0.432768 | 0.239870 | 0.681163 | 0.169560 | 0.717196 | 0.424053 | 0.394500 | 0.245110 | 0.591282 | 0.549406 | 0.623166 | 0.484483 |
| Green energy | 0.578347 | nan | 0.464404 | 0.435934 | 0.711376 | 0.212070 | 0.763814 | 0.470735 | 0.444963 | 0.310991 | 0.599015 | 0.551799 | 0.572353 | 0.499770 |
The relevant difference threshold has been set at a 10.0% difference.
| Green energy | Community solar | diff | outcome | sig | |
|---|---|---|---|---|---|
| variable | |||||
| Adoption | 0.567559 | 0.523424 | 0.044135 | equivalent | 0.000000 |
| Perceived community benefit | 0.599015 | 0.591282 | 0.007733 | equivalent | 0.000000 |
| choice | 0.450624 | 0.451708 | -0.001084 | equivalent | 0.000000 |
| Consideration | 0.435934 | 0.239870 | 0.196064 | non-equivalent | 1.000000 |
| Cost relative usual | 0.444963 | 0.394500 | 0.050463 | equivalent | 0.000000 |
| Belief that others have adopted | 0.212070 | 0.169560 | 0.042510 | equivalent | 0.000000 |
| Self-efficacy | 0.572353 | 0.623166 | -0.050813 | equivalent | 0.000001 |
| Belief that others think people should adopt | 0.470735 | 0.424053 | 0.046682 | equivalent | 0.000000 |
| Intention | 0.310991 | 0.245110 | 0.065882 | equivalent | 0.000193 |
| Program interest | 0.499770 | 0.484483 | 0.015287 | equivalent | 0.000000 |
| Outcome efficacy from purchasing | 0.578347 | 0.587223 | -0.008876 | equivalent | 0.000000 |
| Perceived personal benefit | 0.551799 | 0.549406 | 0.002392 | equivalent | 0.000000 |
| Belief that others should adopt | 0.763814 | 0.717196 | 0.046617 | equivalent | 0.000041 |
| Pay for own utility | 0.944617 | 0.935618 | 0.008999 | equivalent | 0.000000 |
| Responsibility belongs supply-side | 0.464404 | 0.432768 | 0.031636 | equivalent | 0.000000 |
With a threshold of 10%, current results indicate that respondent's only non-equivalent set of answers is for the question of whether they had considered getting green/solar energy in the last 12 months.
Specifically:
'Before taking this survey, had you considered using community solar in the last 12 months?'
To understand the public’s beliefs around EV rentals.
What we want to be able to say
c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
| education | income | age_group | SASSY | politics | state | sex | In the last year, how many separate times have you rented a car? Car rentals can be daily or hourly rentals | In the last year, how many times have you rented a fully electric car? Car rentals can be daily or hourly rentals | Something else - Please specify::Considering the car rentals you made in the last year, what was the primary purpose of your car rental? | Something else - Please specify::What is most important to you when choosing a rental car? | Something else - Please specify::What do you think would be the most important to you if you were to choose a rental car? | Other - Please specify::Where do you think people who rent fully electric vehicles do the majority of their vehicle charging? | Other - Please specify::Where do you think drivers of fully electric cars do the majority of their vehicle charging? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Loading... (need help?) | None | education | income | age_group | SASSY | politics | state | sex | In the last year, how many separate times have you rented a car? Car rentals can be daily or hourly rentals | In the last year, how many times have you rented a fully electric car? Car rentals can be daily or hourly rentals | Something else - Please specify::Considering the car rentals you made in the last year, what was the primary purpose of your car rental? | Something else - Please specify::What is most important to you when choosing a rental car? | Something else - Please specify::What do you think would be the most important to you if you were to choose a rental car? | Other - Please specify::Where do you think people who rent fully electric vehicles do the majority of their vehicle charging? | Other - Please specify::Where do you think drivers of fully electric cars do the majority of their vehicle charging? |
c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
Below you can see a comparison of normalized, weighted averages for each question that was asked on the topic of EVs. Since we wanted to know whether answers in the EV Rental condition were equivalemnt to those in the EV Purchasing condition, we set a bound of +/- 10% on the EV purchasing electricity average and contrasted the EV rental average to it. A significant difference therefore represents a difference in %responses greater than +/- 10%.
| Adoption | Consideration | Available in community | Belief that others have adopted | Belief that others should adopt | Belief that others think people should adopt | Cost relative usual | Perceived community benefit | Perceived personal benefit | Self-efficacy | Program interest | Rents or has car | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| EV Rentals | 0.025409 | 0.574534 | 0.493390 | 0.202461 | 0.395169 | 0.253334 | 0.588914 | 0.442277 | 0.364458 | 0.401100 | 0.372836 | 0.262721 |
| EV Purchases | 0.043618 | 0.674310 | nan | 0.161729 | 0.412537 | 0.296019 | 0.528566 | 0.474137 | 0.441170 | 0.338985 | 0.442289 | 0.184227 |
The relevant difference threshold has been set at a 10.0% difference.
| EV Rentals | EV Purchases | diff | outcome | sig | |
|---|---|---|---|---|---|
| variable | |||||
| Adoption | 0.025409 | 0.033167 | -0.007758 | equivalent | 0.000000 |
| Perceived community benefit | 0.442277 | 0.474137 | -0.031860 | equivalent | 0.000000 |
| Consideration | 0.179026 | 0.377418 | -0.198392 | non-equivalent | 1.000000 |
| Cost relative usual | 0.588914 | 0.528566 | 0.060347 | equivalent | 0.000033 |
| Belief that others have adopted | 0.202461 | 0.161729 | 0.040732 | equivalent | 0.000000 |
| Self-efficacy | 0.401100 | 0.338985 | 0.062114 | equivalent | 0.000187 |
| hasPotential | 0.262721 | 0.909600 | -0.646879 | non-equivalent | 1.000000 |
| Belief that others think people should adopt | 0.253334 | 0.296019 | -0.042684 | equivalent | 0.000000 |
| Program interest | 0.372836 | 0.442289 | -0.069453 | equivalent | 0.004102 |
| Perceived personal benefit | 0.364458 | 0.441170 | -0.076711 | equivalent | 0.015739 |
| Belief that others should adopt | 0.395169 | 0.412537 | -0.017369 | equivalent | 0.000000 |
With a threshold of 10%, current results indicate that respondent's only non-equivalent set of answers is for the question of whether they had considered getting green/solar energy in the last 12 months.
Specifically:
To understand who people believe might be against talking about these actions.
What we want to be able to say
The scores presented in the table represent the weighted average of respondents' likert responses. Resondents were given a 7-item likert scale for which to answer from Strongly agree to Strongly disagree, the table below showes the weighted average of normalized responses. That is, 1 represents an average responses of Strongly agree, whilst 0 represents an average response of Strongly disagree.
What are some of the steps and reasons to reduce meat consumption given by the respondents?
| education | income | age_group | SASSY | politics | state | sex | Are you trying to limit your meat consumption? | Has successfully reduced meat eating in the last 5 years | Why are you trying to reduce/limit your meat consumption? | Can you tell us a bit more about the steps you are taking to reduce/limit your meat consumption? | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Loading... (need help?) | None | education | income | age_group | SASSY | politics | state | sex | Are you trying to limit your meat consumption? | Has successfully reduced meat eating in the last 5 years | Why are you trying to reduce/limit your meat consumption? | Can you tell us a bit more about the steps you are taking to reduce/limit your meat consumption? |
Respondents were asked wiith whom the had shared they were trying to eat less meat. The table represents the % respondents who intend to reduce their meat intake and who have shared with X member of their community.
They were also asked whether they thought their community would approve or dissaprove of their decision to reduce meat consumption.
| Of the following groups of people in your life, which ones do you think know that you are trying to eat less meat? | |
|---|---|
| Your family | 76.392758 |
| Your close friends | 54.101835 |
| Your colleagues | 14.979179 |
| Your neighbors | 12.499653 |
| Your social media followers | 10.559358 |
c:\Anaconda\envs\pandas-profiling\lib\site-packages\numpy\core\_asarray.py:102: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray.
| education | income | age_group | SASSY | politics | state | sex | Are you trying to limit your meat consumption? | Has successfully reduced meat eating in the last 5 years | Other - Please specify::Of the following groups of people in your life, which ones do you think know that you are trying to eat less meat? | Why have you shared, or why haven't you shared with others? | Please tell us who or which groups of people that would that be? | Who's guidance/tips would you prefer to receive about reducing your meat consumption? | Why would you prefer to receive advice from the group you selected? | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Loading... (need help?) | None | education | income | age_group | SASSY | politics | state | sex | Are you trying to limit your meat consumption? | Has successfully reduced meat eating in the last 5 years | Other - Please specify::Of the following groups of people in your life, which ones do you think know that you are trying to eat less meat? | Why have you shared, or why haven't you shared with others? | Please tell us who or which groups of people that would that be? | Who's guidance/tips would you prefer to receive about reducing your meat consumption? | Why would you prefer to receive advice from the group you selected? |
| How much do you agree or disagree with the following statement? | |
|---|---|
| I am interested in learning how reducing my meat consumption can help my health and wellbeing | 0.570174 |
| I am interested in learning how reducing my meat consumption can benefit the environment | 0.517047 |
| I am interested in learning how reducing my meat consumption can benefit me financially | 0.537400 |
| I think positively about organizations that champion healthy food | 0.673114 |
| I think positively about organizations that champion environmentally sustainable food | 0.628164 |
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